Query the Data Delivery Network
Query the DDNThe easiest way to query any data on Splitgraph is via the "Data Delivery Network" (DDN). The DDN is a single endpoint that speaks the PostgreSQL wire protocol. Any Splitgraph user can connect to it at data.splitgraph.com:5432 and query any version of over 40,000 datasets that are hosted or proxied by Splitgraph.
For example, you can query the successful_naloxone_reversals_by_law_enforcement table in this repository, by referencing it like:
"pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d:latest"."successful_naloxone_reversals_by_law_enforcement"or in a full query, like:
SELECT
":id", -- Socrata column ID
"county_code", -- Two-digit code which uniquely identifies each county. 01=Adams, 02=Allegheny,…67=York. Not applicable. No FIPS code or county code exist for Pennsylvania State Police and Capitol Police. '00'- Commonwealth of Pennsylvania
"county_fips_code", -- The FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code (FIPS 6-4) which uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. 42000=Pennsylvania, 42001=Adams, 42003=Allegheny,…42133=York. Not applicable. No FIPS code or county code exist for Pennsylvania State Police and Capitol Police.
"county", -- Geographic region in Pennsylvania representing where the naloxone reversal event took place.
"location_zip",
"location_state",
"number_of_successful_reversals", -- Total number of successful naloxone reversals during the period indicated for that year. If the field is null, no data was collected.
"date_revised", -- Date (month/day/year) of most recent data collection per county. Most data are revised quarterly.
"location_city",
"location_address",
":@computed_region_amqz_jbr4",
":@computed_region_nmsq_hqvv",
":@computed_region_r6rf_p9et",
":@computed_region_rayf_jjgk",
":@computed_region_d3gw_znnf",
"year", -- Represents calendar year (January- December) for 2016 and 2017 data collection. 2014-2015 data represents November 2014-December 2015, and 2014-2017 data represents November 2014-December 2017, as data collection began in November 2014.
"location", -- Latitude and longitude; one random point within each county to help create a map of Pennsylvania counties and define boundary lines.
"police_coverage" -- Percentage of police departments within each county whose officers carry naloxone. “No coverage” means there are municipal PD located in those counties, but 0% carry naloxone. “Partial coverage” means some, but not all (1-99%) of the municipal PD in those counties carry naloxone. “Full coverage” means that all (100%) of municipal PDs in those counties carry naloxone. Prior to 2017 data was not collected and stored
FROM
"pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d:latest"."successful_naloxone_reversals_by_law_enforcement"
LIMIT 100;Connecting to the DDN is easy. All you need is an existing SQL client that can connect to Postgres. As long as you have a SQL client ready, you'll be able to query pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d with SQL in under 60 seconds.
Query Your Local Engine
bash -c "$(curl -sL https://github.com/splitgraph/splitgraph/releases/latest/download/install.sh)"Read the installation docs.
Splitgraph Cloud is built around Splitgraph Core (GitHub), which includes a local Splitgraph Engine packaged as a Docker image. Splitgraph Cloud is basically a scaled-up version of that local Engine. When you query the Data Delivery Network or the REST API, we mount the relevant datasets in an Engine on our servers and execute your query on it.
It's possible to run this engine locally. You'll need a Mac, Windows or Linux system to install sgr, and a Docker installation to run the engine. You don't need to know how to actually use Docker; sgrcan manage the image, container and volume for you.
There are a few ways to ingest data into the local engine.
For external repositories, the Splitgraph Engine can "mount" upstream data sources by using sgr mount. This feature is built around Postgres Foreign Data Wrappers (FDW). You can write custom "mount handlers" for any upstream data source. For an example, we blogged about making a custom mount handler for HackerNews stories.
For hosted datasets (like this repository), where the author has pushed Splitgraph Images to the repository, you can "clone" and/or "checkout" the data using sgr cloneand sgr checkout.
Cloning Data
Because pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d:latest is a Splitgraph Image, you can clone the data from Spltgraph Cloud to your local engine, where you can query it like any other Postgres database, using any of your existing tools.
First, install Splitgraph if you haven't already.
Clone the metadata with sgr clone
This will be quick, and does not download the actual data.
sgr clone pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87dCheckout the data
Once you've cloned the data, you need to "checkout" the tag that you want. For example, to checkout the latest tag:
sgr checkout pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d:latestThis will download all the objects for the latest tag of pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d and load them into the Splitgraph Engine. Depending on your connection speed and the size of the data, you will need to wait for the checkout to complete. Once it's complete, you will be able to query the data like you would any other Postgres database.
Alternatively, use "layered checkout" to avoid downloading all the data
The data in pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d:latest is 0 bytes. If this is too big to download all at once, or perhaps you only need to query a subset of it, you can use a layered checkout.:
sgr checkout --layered pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d:latestThis will not download all the data, but it will create a schema comprised of foreign tables, that you can query as you would any other data. Splitgraph will lazily download the required objects as you query the data. In some cases, this might be faster or more efficient than a regular checkout.
Read the layered querying documentation to learn about when and why you might want to use layered queries.
Query the data with your existing tools
Once you've loaded the data into your local Splitgraph Engine, you can query it with any of your existing tools. As far as they're concerned, pa-gov/successful-naloxone-reversals-by-law-enforcement-e9n3-x87d is just another Postgres schema.